42 posts tagged with "Industrial IoT"

Predictive maintenance isn't a futuristic concept anymore — it's the standard that separates world-class manufacturing operations from the ones bleeding money on unplanned downtime. If your plant still runs on reactive or calendar-based maintenance, you're leaving between 10% and 40% of your maintenance budget on the table, according to the U.S. Department of Energy.

This guide walks you through exactly how to implement predictive maintenance in a real manufacturing environment — no academic theory, no vendor hand-waving. Just practical steps from someone who's done it.

Your PLCs are already collecting everything you need — temperature, pressure, cycle counts, motor current, alarm states. The problem is that data lives in a controller on the factory floor, visible only to whoever's standing in front of the HMI. Connecting your PLCs to the cloud unlocks real-time visibility, predictive maintenance, and fleet-wide analytics across every plant.

This guide covers the practical reality of doing it — not the whiteboard architecture, but the actual engineering decisions, protocol considerations, and pitfalls you'll hit along the way.

Industrial IoT sounds complicated. The reality is simpler than most vendors make it appear. At its core, IIoT is about connecting your factory equipment to the internet so you can see what's happening — in real time, from anywhere, with data you can actually use to make better decisions.

If you're a plant manager, maintenance engineer, or operations leader who's been hearing about IIoT but hasn't started yet, this guide is for you. No jargon walls, no PhD-level concepts. Just the practical foundation you need to go from "I should probably look into this" to "we have our first machines connected and delivering value."

"Digital twin" has become one of the most overused terms in manufacturing technology. Depending on who you ask, it means anything from a 3D visualization of a factory to a physics-based simulation that predicts equipment failure to a complete virtual replica that runs in parallel with the physical plant. The term has been stretched so far that it's almost meaningless.

This guide brings it back to earth. We'll define what a digital twin actually is in a manufacturing context, explain the different maturity levels, and give you a practical roadmap for building one — starting with what you can do this month, not what you might do in five years.

Every industrial automation vendor now claims to be "AI-powered." Every conference keynote promises autonomous factories. Every analyst report projects trillions in AI-driven manufacturing value by 2030. And yet most plant managers you talk to will tell you their biggest maintenance tool is still a clipboard and a walkie-talkie.

The gap between the AI hype in manufacturing and the on-the-ground reality is enormous. This article separates the signal from the noise — based on what we've actually seen working on factory floors, not what looks good in a pitch deck.

Every second, a modern manufacturing plant generates millions of data points. PLC registers cycle through readings, sensors capture temperatures and pressures, vision systems inspect parts, and motor drives report speed and torque values. The question isn't whether to collect this data — it's where to process it.

For the past decade, the default answer was "send everything to the cloud." But manufacturers are learning the hard way that shipping every data point from every machine to a cloud server creates problems: network bandwidth costs, latency that prevents real-time action, dependency on internet connectivity, and enormous cloud compute bills.

Edge computing — processing data at or near the source — is emerging as the practical answer for most manufacturing IIoT applications. Here's why it matters, how it works, and what to consider for your factory.

MachineMetrics has built a strong reputation in the CNC machine monitoring space, delivering real-time OEE tracking and production visibility for discrete manufacturers. MachineCDN takes a broader approach, providing AI-powered predictive maintenance across all types of manufacturing equipment — from CNC machines and injection molders to compressors, pumps, conveyors, and HVAC systems. This comparison helps manufacturers understand when CNC-specific monitoring is enough and when you need a full-factory predictive maintenance platform.

Samsara and MachineCDN both appear in IIoT platform evaluations, but they serve fundamentally different markets. Samsara built its business on fleet management — GPS tracking, driver safety cameras, and vehicle telematics — and has been expanding into facility and equipment monitoring. MachineCDN was purpose-built for manufacturing, delivering AI-powered predictive maintenance with plug-and-play hardware and cellular connectivity. This comparison helps manufacturers understand where each platform excels and which one actually solves manufacturing problems.

Litmus has established itself as a recognized edge computing platform for industrial environments, earning a Gartner Challenger position in the IIoT Platforms category. But Litmus isn't the right fit for every manufacturer. Whether you're concerned about deployment complexity, total cost of ownership, the lack of built-in analytics, or simply want to evaluate options before committing, this guide covers the best Litmus alternatives for 2026 — evaluated through the lens of what matters most to manufacturing operations teams.

If you've been evaluating Litmus for your IIoT deployment, you've probably noticed something: there's no pricing page on litmus.io. That's intentional. Like most enterprise IIoT platforms, Litmus uses custom pricing that varies based on deployment size, features, and negotiation leverage. This guide provides the most comprehensive breakdown of Litmus pricing available in 2026, based on publicly available information, industry benchmarking, and feedback from manufacturing teams who have gone through the Litmus procurement process.